SurveyMonkey Example Data Analysis
Zack Crowley
2024-07-11
- Start of new vignette:
Importing and Cleaning Dataname file: ‘import_clean.Rmd’
Data Cleaning and Manipulation
Importing data from SurveyMonkey
Our current survey provider is SurveyMonkey,
blackstone contains several functions that makes the
process of reading SurveyMonkey data into R a more
manageable process and creates a codebook for the data along the
way.
SurveyMonkey exports data with two header rows, which does not work
with R where tibbles and dataframes can only have one row
of names.
Here is how to import data from SurveyMonkey using example data
provided with blackstone, this is a fake dataset of a pre
(baseline) survey. There are three steps to this process:
- Create a codebook.
- Edit and save the codebook to create meaningful variable names.
- Read in the data and rename the variables with the codebook.
Pre Survey Data
1. Creating the codebook
# File path for pre example data:
pre_data_fp <- blackstone::blackstoneExample("sm_data_pre.csv")
# 1. Create the codebook:
codebook_pre <- blackstone::createCodebook(pre_data_fp)
codebook_pre
## # A tibble: 32 × 5
## header_1 header_2 combined_header position variable_name
## <chr> <chr> <chr> <int> <chr>
## 1 Respondent ID <NA> Respondent ID 1 respondent_id
## 2 Collector ID <NA> Collector ID 2 collector_id
## 3 Start Date <NA> Start Date 3 start_date
## 4 End Date <NA> End Date 4 end_date
## 5 IP Address <NA> IP Address 5 ip_address
## # ℹ 27 more rowsFor this codebook, the first column header_1 is the
first header from the SurveyMonkey data, the second column
header_2 is the second header, the third column
combined_header is the combination of the two headers,
position is the column number position for each
combined_header, and variable_name is a
cleaned up version for combined_header and will be the
column to edit to change the column names later on to shorter and more
meaningful names.
variable_name will be the column that renames all the
variables in the SurveyMonkey data.
2. Editing and Saving the Codebook
# Step 2. Edit the codebook:
# Set up sequential naming convections for matrix-style questions with shared likert scale response options:
# 8 items that are matrix-style likert scales- turned into a scale called `research`- here is how to easily name them all at once:
# Rows 11 to 18 belong to the "research" matrix question (you will have to look at the codebook and match the header_1 and header_2 to variable_name to change)
research_items <- codebook_pre[["variable_name"]][11:18]
research_names <- paste0("research_", seq_along(research_items)) %>% purrr::set_names(., research_items) # Create a new named vector of names for these columns
# 6 items that are matrix-style likert scales- turned into a scale called `ability`- Rows 19 to 24 named `variable_name`:
ability_items <- codebook_pre[["variable_name"]][19:24]
ability_names <- paste0("ability_", seq_along(ability_items)) %>% purrr::set_names(., ability_items) # Create a new named vector of names for these columns
# 6 items that are matrix-style likert scales- turned into a scale called `ethics`- Rows 19 to 24 named `variable_name`:
ethics_items <- codebook_pre[["variable_name"]][25:29]
ethics_names <- paste0("ethics_", seq_along(ethics_items)) %>% purrr::set_names(., ethics_items) # Create a new named vector of names for these columns
# Edit the `variable_names` column: Use dplyr::mutate() and dplyr::case_match() to change the column `variable_name`:
codebook_pre <- codebook_pre %>% dplyr::mutate(
variable_name = dplyr::case_match(
variable_name, # column to match
'custom_data_1' ~ "unique_id", # changes 'custom_data_1' to "unique_id"
'to_what_extent_are_you_knowledgeable_in_conducting_research_in_your_field_of_study' ~ "knowledge",
'with_which_gender_do_you_most_closely_identify' ~ "gender",
'which_race_ethnicity_best_describes_you_please_choose_only_one' ~ "ethnicity",
'are_you_a_first_generation_college_student' ~ "first_gen",
names(research_names) ~ research_names[variable_name], # takes the above named vector and when the name matches, applies new value in that position as replacement.
names(ability_names) ~ ability_names[variable_name], # Same for `ability_names`
names(ethics_names) ~ ethics_names[variable_name], # Same for `ability_names`
.default = variable_name # returns default value from original `variable_name` if not changed.
)
)
codebook_pre
## # A tibble: 32 × 5
## header_1 header_2 combined_header position variable_name
## <chr> <chr> <chr> <int> <chr>
## 1 Respondent ID <NA> Respondent ID 1 respondent_id
## 2 Collector ID <NA> Collector ID 2 collector_id
## 3 Start Date <NA> Start Date 3 start_date
## 4 End Date <NA> End Date 4 end_date
## 5 IP Address <NA> IP Address 5 ip_address
## # ℹ 27 more rows
# Write out the edited codebook to save for future use-
# Be sure to double check questions match new names before writing out:
# readr::write_csv(codebook_pre, file = "{filepath-to-codebok}")3. Import the Data and Rename the Variables with the Codebook
# 3. Read in the data and rename the vars using readRenameData(), passing the file path and the edited codebook:
pre_data <- blackstone::readRenameData(pre_data_fp, codebook = codebook_pre)
pre_data
## # A tibble: 100 × 32
## respondent_id collector_id start_date end_date ip_address email_address
## <dbl> <dbl> <date> <date> <chr> <chr>
## 1 114628000001 431822954 2024-06-05 2024-06-06 227.224.138.113 coraima59@me…
## 2 114628000002 431822954 2024-06-21 2024-06-22 110.241.132.50 mstamm@hermi…
## 3 114628000003 431822954 2024-06-14 2024-06-15 165.58.112.64 precious.fei…
## 4 114628000004 431822954 2024-06-15 2024-06-16 49.34.121.147 ines52@gmail…
## 5 114628000005 431822954 2024-06-15 2024-06-16 115.233.66.80 franz44@hotm…
## # ℹ 95 more rows
## # ℹ 26 more variables: first_name <chr>, last_name <chr>, unique_id <dbl>,
## # knowledge <chr>, research_1 <chr>, research_2 <chr>, research_3 <chr>,
## # research_4 <chr>, research_5 <chr>, research_6 <chr>, research_7 <chr>,
## # research_8 <chr>, ability_1 <chr>, ability_2 <chr>, ability_3 <chr>,
## # ability_4 <chr>, ability_5 <chr>, ability_6 <chr>, ethics_1 <chr>,
## # ethics_2 <chr>, ethics_3 <chr>, ethics_4 <chr>, ethics_5 <chr>, …The SurveyMonkey example data is now imported with names taken from
the codebook column variable_name:
names(pre_data)
## [1] "respondent_id" "collector_id" "start_date" "end_date"
## [5] "ip_address" "email_address" "first_name" "last_name"
## [9] "unique_id" "knowledge" "research_1" "research_2"
## [13] "research_3" "research_4" "research_5" "research_6"
## [17] "research_7" "research_8" "ability_1" "ability_2"
## [21] "ability_3" "ability_4" "ability_5" "ability_6"
## [25] "ethics_1" "ethics_2" "ethics_3" "ethics_4"
## [29] "ethics_5" "gender" "ethnicity" "first_gen"Post Survey Data
Do that same process over again with the post data, if the variables are all the same you can use the same codebook.
For this example there are additional post variables so a new codebook will need to be created to rename the variables when reading in the data with
readRenameData().
# File path for pre example data:
post_data_fp <- blackstone::blackstoneExample("sm_data_post.csv")
# 1. Create the codebook using the filepath:
codebook_post <- blackstone::createCodebook(post_data_fp)
codebook_post
## # A tibble: 37 × 5
## header_1 header_2 combined_header position variable_name
## <chr> <chr> <chr> <int> <chr>
## 1 Respondent ID <NA> Respondent ID 1 respondent_id
## 2 Collector ID <NA> Collector ID 2 collector_id
## 3 Start Date <NA> Start Date 3 start_date
## 4 End Date <NA> End Date 4 end_date
## 5 IP Address <NA> IP Address 5 ip_address
## # ℹ 32 more rows
# Step 2. Edit the codebook:
# Set up sequential naming convections for matrix-style questions with shared likert scale response options:
# 8 items that are matrix-style likert scales- turned into a scale called `research`- here is how to easily name them all at once:
# Rows 11 to 18 belong to the "research" matrix question (you will have to look at the codebook and match the header_1 and header_2 to variable_name to change)
research_items <- codebook_post[["variable_name"]][11:18]
research_names <- paste0("research_", seq_along(research_items)) %>% purrr::set_names(., research_items) # Create a new named vector of names for these columns
# 6 items that are matrix-style likert scales- turned into a scale called `ability`- Rows 19 to 24 named `variable_name`:
ability_items <- codebook_post[["variable_name"]][19:24]
ability_names <- paste0("ability_", seq_along(ability_items)) %>% purrr::set_names(., ability_items) # Create a new named vector of names for these columns
# 6 items that are matrix-style likert scales- turned into a scale called `ethics`- Rows 19 to 24 named `variable_name`:
ethics_items <- codebook_post[["variable_name"]][25:29]
ethics_names <- paste0("ethics_", seq_along(ethics_items)) %>% purrr::set_names(., ethics_items) # Create a new named vector of names for these columns
# 5 items that are Open-ended follow up when corresponeding ethics items were answered "Strongly disagree"or "Disagree"- Rows 30 to 34 named `variable_name`:
ethics_items_oe <- codebook_post[["variable_name"]][30:34]
ethics_names_oe <- paste0("ethics_", seq_along(ethics_items), "_oe") %>% purrr::set_names(., ethics_items_oe) # Create a new named vector of names for these columns
# Edit the `variable_names` column: Use dplyr::mutate() and dplyr::case_match() to change the column `variable_name`:
codebook_post <- codebook_post %>% dplyr::mutate(
variable_name = dplyr::case_match(
variable_name, # column to match
'custom_data_1' ~ "unique_id", # changes 'custom_data_1' to "unique_id"
'to_what_extent_are_you_knowledgeable_in_conducting_research_in_your_field_of_study' ~ "knowledge",
'with_which_gender_do_you_most_closely_identify' ~ "gender",
'which_race_ethnicity_best_describes_you_please_choose_only_one' ~ "ethnicity",
'are_you_a_first_generation_college_student' ~ "first_gen",
names(research_names) ~ research_names[variable_name], # takes the above named vector and when the name matches, applies new value in that position as replacement.
names(ability_names) ~ ability_names[variable_name], # Same for `ability_names`
names(ethics_names) ~ ethics_names[variable_name], # Same for `ability_names`
names(ethics_names_oe) ~ ethics_names_oe[variable_name], # Same for `ethics_names_oe`
.default = variable_name # returns default value from original `variable_name` if not changed.
)
)
codebook_post
## # A tibble: 37 × 5
## header_1 header_2 combined_header position variable_name
## <chr> <chr> <chr> <int> <chr>
## 1 Respondent ID <NA> Respondent ID 1 respondent_id
## 2 Collector ID <NA> Collector ID 2 collector_id
## 3 Start Date <NA> Start Date 3 start_date
## 4 End Date <NA> End Date 4 end_date
## 5 IP Address <NA> IP Address 5 ip_address
## # ℹ 32 more rows
# Write out the edited codebook to save for future use-
# Be sure to double check questions match new names before writing out:
# readr::write_csv(codebook_post, file = "{filepath-to-codebok}")
# 3. Read in the data and rename the vars using readRenameData(), passing the file path and the edited codebook:
post_data <- blackstone::readRenameData(post_data_fp, codebook = codebook_post)
post_data
## # A tibble: 100 × 37
## respondent_id collector_id start_date end_date ip_address email_address
## <dbl> <dbl> <date> <date> <chr> <chr>
## 1 114628000001 431822954 2024-06-05 2024-06-06 227.224.138.113 coraima59@me…
## 2 114628000002 431822954 2024-06-21 2024-06-22 110.241.132.50 mstamm@hermi…
## 3 114628000003 431822954 2024-06-14 2024-06-15 165.58.112.64 precious.fei…
## 4 114628000004 431822954 2024-06-15 2024-06-16 49.34.121.147 ines52@gmail…
## 5 114628000005 431822954 2024-06-15 2024-06-16 115.233.66.80 franz44@hotm…
## # ℹ 95 more rows
## # ℹ 31 more variables: first_name <chr>, last_name <chr>, unique_id <dbl>,
## # knowledge <chr>, research_1 <chr>, research_2 <chr>, research_3 <chr>,
## # research_4 <chr>, research_5 <chr>, research_6 <chr>, research_7 <chr>,
## # research_8 <chr>, ability_1 <chr>, ability_2 <chr>, ability_3 <chr>,
## # ability_4 <chr>, ability_5 <chr>, ability_6 <chr>, ethics_1 <chr>,
## # ethics_2 <chr>, ethics_3 <chr>, ethics_4 <chr>, ethics_5 <chr>, …Finally, it is important to add pre_ and
post_ prefixes to all unique variables before merging the
datasets, (i.e. the survey items that differ pre-post- the SM items and
demos are identical):
# Pre data:
pre_data <- pre_data %>% dplyr::rename_with(~ paste0("pre_", .), .cols = c(knowledge:ethics_5))
# Pre data:
post_data <- post_data %>% dplyr::rename_with(~ paste0("post_", .), .cols = c(knowledge:ethics_5_oe))Merging Data
Merge pre-post data by joining on all the variables that are shared in common.
The {dplyr} package has many joining
functions, the most commonly use is dplyr::left_join()
which keeps all the observations from the first table provided and
merges all observations from the second that match.
For most data analysis, we will want to use the post data as the primary table and merge all the pre data since most post surveys drop some participants, so we can run our analysis on complete data.
# left_join() will automatically join by all the shared columns, be sure to include all shared variables that should be identical pre-post to the 'by = join_by()' as an arg
# (otherwise you will get a message about additional variables to be joined by):
sm_data <- post_data %>% dplyr::left_join(pre_data, by = dplyr::join_by(respondent_id, collector_id, start_date, end_date, ip_address, email_address,
first_name, last_name, unique_id, gender, ethnicity, first_gen))
sm_data
## # A tibble: 100 × 57
## respondent_id collector_id start_date end_date ip_address email_address
## <dbl> <dbl> <date> <date> <chr> <chr>
## 1 114628000001 431822954 2024-06-05 2024-06-06 227.224.138.113 coraima59@me…
## 2 114628000002 431822954 2024-06-21 2024-06-22 110.241.132.50 mstamm@hermi…
## 3 114628000003 431822954 2024-06-14 2024-06-15 165.58.112.64 precious.fei…
## 4 114628000004 431822954 2024-06-15 2024-06-16 49.34.121.147 ines52@gmail…
## 5 114628000005 431822954 2024-06-15 2024-06-16 115.233.66.80 franz44@hotm…
## # ℹ 95 more rows
## # ℹ 51 more variables: first_name <chr>, last_name <chr>, unique_id <dbl>,
## # post_knowledge <chr>, post_research_1 <chr>, post_research_2 <chr>,
## # post_research_3 <chr>, post_research_4 <chr>, post_research_5 <chr>,
## # post_research_6 <chr>, post_research_7 <chr>, post_research_8 <chr>,
## # post_ability_1 <chr>, post_ability_2 <chr>, post_ability_3 <chr>,
## # post_ability_4 <chr>, post_ability_5 <chr>, post_ability_6 <chr>, …Data Cleaning
Convert all likert scales to factors (for ordering) and all demographics.
Create numeric variables from the factor variables for use with statistical tests later on.
If applicable, drop all “Missing”/
NAobservations.
## Knowledge scale
levels_knowledge <- c("Not knowledgeable at all", "A little knowledgeable", "Somewhat knowledgeable", "Very knowledgeable", "Extremely knowledgeable")
## Research Items scale:
levels_confidence <- c("Not at all confident", "Slightly confident", "Somewhat confident", "Very confident", "Extremely confident")
## Ability Items scale:
levels_min_ext <- c("Minimal", "Slight", "Moderate", "Good", "Extensive")
## Ethics Items scale:
levels_agree5 <- c("Strongly disagree", "Disagree", "Neither agree nor disagree", "Agree", "Strongly agree")
# Demographic levels:
gender_levels <- c("Female","Male","Non-binary", "Do not wish to specify")
ethnicity_levels <- c("White (Non-Hispanic/Latino)", "Asian", "Black", "Hispanic or Latino", "American Indian or Alaskan Native",
"Native Hawaiian or other Pacific Islander", "Do not wish to specify")
first_gen_levels <- c("Yes", "No", "I'm not sure")
# Use mutate() for convert each item in each scale to a factor with vectors above, across() will perform a function for items selected using contains() or can be selected
# by variables names individually using a character vector: _knowledge or use c("pre_knowledg","post_knowledge")
# Also create new numeric variables for all the likert scale items and use the suffix '_num' to denote numeric:
sm_data <- sm_data %>% dplyr::mutate(dplyr::across(tidyselect::contains("_knowledge"), ~ factor(., levels = levels_knowledge)), # match each name pattern to select to each factor level
dplyr::across(tidyselect::contains("_knowledge"), as.numeric, .names = "{.col}_num"), # create new numeric items for all knowledge items
dplyr::across(tidyselect::contains("research_"), ~ factor(., levels = levels_confidence)),
dplyr::across(tidyselect::contains("research_"), as.numeric, .names = "{.col}_num"), # create new numeric items for all research items
dplyr::across(tidyselect::contains("ability_"), ~ factor(., levels = levels_min_ext)),
dplyr::across(tidyselect::contains("ability_"), as.numeric, .names = "{.col}_num"), # create new numeric items for all ability items
# select ethics items but not the open_ended responses:
dplyr::across(tidyselect::contains("ethics_") & !tidyselect::contains("_oe"), ~ factor(., levels = levels_agree5)),
dplyr::across(tidyselect::contains("ethics_") & !tidyselect::contains("_oe"), as.numeric, .names = "{.col}_num"), # new numeric items for all ethics items
# individually convert all demographics to factor variables:
gender = factor(gender, levels = gender_levels),
ethnicity = factor(ethnicity, levels = ethnicity_levels),
first_gen = factor(first_gen, levels = first_gen_levels),
)
sm_data
## # A tibble: 100 × 97
## respondent_id collector_id start_date end_date ip_address email_address
## <dbl> <dbl> <date> <date> <chr> <chr>
## 1 114628000001 431822954 2024-06-05 2024-06-06 227.224.138.113 coraima59@me…
## 2 114628000002 431822954 2024-06-21 2024-06-22 110.241.132.50 mstamm@hermi…
## 3 114628000003 431822954 2024-06-14 2024-06-15 165.58.112.64 precious.fei…
## 4 114628000004 431822954 2024-06-15 2024-06-16 49.34.121.147 ines52@gmail…
## 5 114628000005 431822954 2024-06-15 2024-06-16 115.233.66.80 franz44@hotm…
## # ℹ 95 more rows
## # ℹ 91 more variables: first_name <chr>, last_name <chr>, unique_id <dbl>,
## # post_knowledge <fct>, post_research_1 <fct>, post_research_2 <fct>,
## # post_research_3 <fct>, post_research_4 <fct>, post_research_5 <fct>,
## # post_research_6 <fct>, post_research_7 <fct>, post_research_8 <fct>,
## # post_ability_1 <fct>, post_ability_2 <fct>, post_ability_3 <fct>,
## # post_ability_4 <fct>, post_ability_5 <fct>, post_ability_6 <fct>, …The data cleaned in this vignette will be used as the example data in
the vignettes Data analysis and Statistical Inference
and Data Visualization to
further showcase all the functions contained in
blackstone.
- end of vignette
Importing and Cleaning Data
Data analysis and Statistical Inference
- Start of new vignette:
Data analysis and Statistical Inferencename file: ‘analysis.Rmd’
Likert Scale Table
The most common task is creating frequency tables of counts and
percentages for likert scale items, blackstone has the
likertTable() for that:
# Research items pre and post frequency table, with counts and percentages: use levels_confidence character vector
# use likertTable to return frequency table, passing the scale_labels: (can also label the individual questions using the arg question_label)
sm_data %>% dplyr::select(tidyselect::contains("research_") & !tidyselect::contains("_num") & where(is.factor)) %>%
blackstone::likertTable(., scale_labels = levels_confidence)Question | Not at all | Slightly | Somewhat | Very | Extremely | n |
|---|---|---|---|---|---|---|
post_research_1 | 2 (2%) | 26 (26%) | 23 (23%) | 25 (25%) | 24 (24%) | 100 |
post_research_2 | 12 (12%) | 14 (14%) | 12 (12%) | 14 (14%) | 48 (48%) | 100 |
post_research_3 | 8 (8%) | 22 (22%) | 21 (21%) | 23 (23%) | 26 (26%) | 100 |
post_research_4 | 2 (2%) | 24 (24%) | 25 (25%) | 19 (19%) | 30 (30%) | 100 |
post_research_5 | 14 (14%) | 19 (19%) | 19 (19%) | 19 (19%) | 29 (29%) | 100 |
post_research_6 | 5 (5%) | 3 (3%) | 24 (24%) | 28 (28%) | 40 (40%) | 100 |
post_research_7 | 11 (11%) | 10 (10%) | 14 (14%) | 17 (17%) | 48 (48%) | 100 |
post_research_8 | 4 (4%) | 7 (7%) | 23 (23%) | 23 (23%) | 43 (43%) | 100 |
pre_research_1 | 10 (10%) | 8 (8%) | 41 (41%) | 27 (27%) | 14 (14%) | 100 |
pre_research_2 | 56 (56%) | 19 (19%) | 8 (8%) | 9 (9%) | 8 (8%) | 100 |
pre_research_3 | 32 (32%) | 23 (23%) | 15 (15%) | 20 (20%) | 10 (10%) | 100 |
pre_research_4 | 9 (9%) | 24 (24%) | 32 (32%) | 24 (24%) | 11 (11%) | 100 |
pre_research_5 | 40 (40%) | 18 (18%) | 21 (21%) | 13 (13%) | 8 (8%) | 100 |
pre_research_6 | 17 (17%) | 25 (25%) | 25 (25%) | 16 (16%) | 17 (17%) | 100 |
pre_research_7 | 59 (59%) | 13 (13%) | 11 (11%) | 10 (10%) | 7 (7%) | 100 |
pre_research_8 | 21 (21%) | 19 (19%) | 23 (23%) | 19 (19%) | 18 (18%) | 100 |
Using Functional Programming to Speed Up Analysis
Here is one approach to use functional programming from the
{purrr} package create many frequency tables at once:
# Another way to make a list of many freq_tables to print out with other data analysis later on,
# using pmap() to do multiple likertTable() at once:
# Set up tibbles of each set of scales that contain all pre and post data:
# research:
research_df <- sm_data %>% dplyr::select(tidyselect::contains("research_") & !tidyselect::contains("_num") & where(is.factor))
# knowledge:
knowledge_df <- sm_data %>% dplyr::select(tidyselect::contains("_knowledge") & !tidyselect::contains("_num") & where(is.factor))
# ability:
ability_df <- sm_data %>% dplyr::select(tidyselect::contains("ability_") & !tidyselect::contains("_num") & where(is.factor))
# ethics:
ethics_df <- sm_data %>% dplyr::select(tidyselect::contains("ethics_") & !tidyselect::contains("_oe") & !tidyselect::contains("_num") & where(is.factor))
# set up tibble with the columns as the args to pass to likertTable(), each row of the column `df` is the tibble of items and
# each row of `scale_labels` is the vector of likert scale labels:
freq_params <- tibble::tribble(
~df, ~scale_labels, # name of columns (these need to match the names of the arguments in the function that you want to use later in `purrr::pmap()`)
knowledge_df, levels_knowledge,
research_df, levels_confidence,
ability_df, levels_min_ext,
ethics_df, levels_agree5
)
# Create a named list of frequency tables by using `purrr::pmap()` which takes in a tibble where each column is an argument that is passed to the function, and
# each row is contains the inputs for a single output, so here each row will be one frequency table that is return to a list and named for easy retrieval later on:
freq_tables <- freq_params %>% purrr::pmap(blackstone::likertTable) %>%
purrr::set_names(., c("Knowledge Items", "Research Items", "Ability Items", "Ethics Items"))
# Can select the list by position or by name:
# freq_tables[[1]] # by list position
freq_tables[["Knowledge Items"]] # by nameQuestion | Not | A little | Somewhat | Very | Extremely | n |
|---|---|---|---|---|---|---|
post_knowledge | 7 (7%) | 14 (14%) | 9 (9%) | 24 (24%) | 46 (46%) | 100 |
pre_knowledge | 65 (65%) | 11 (11%) | 9 (9%) | 9 (9%) | 6 (6%) | 100 |
freq_tables[["Research Items"]]Question | Not at all | Slightly | Somewhat | Very | Extremely | n |
|---|---|---|---|---|---|---|
post_research_1 | 2 (2%) | 26 (26%) | 23 (23%) | 25 (25%) | 24 (24%) | 100 |
post_research_2 | 12 (12%) | 14 (14%) | 12 (12%) | 14 (14%) | 48 (48%) | 100 |
post_research_3 | 8 (8%) | 22 (22%) | 21 (21%) | 23 (23%) | 26 (26%) | 100 |
post_research_4 | 2 (2%) | 24 (24%) | 25 (25%) | 19 (19%) | 30 (30%) | 100 |
post_research_5 | 14 (14%) | 19 (19%) | 19 (19%) | 19 (19%) | 29 (29%) | 100 |
post_research_6 | 5 (5%) | 3 (3%) | 24 (24%) | 28 (28%) | 40 (40%) | 100 |
post_research_7 | 11 (11%) | 10 (10%) | 14 (14%) | 17 (17%) | 48 (48%) | 100 |
post_research_8 | 4 (4%) | 7 (7%) | 23 (23%) | 23 (23%) | 43 (43%) | 100 |
pre_research_1 | 10 (10%) | 8 (8%) | 41 (41%) | 27 (27%) | 14 (14%) | 100 |
pre_research_2 | 56 (56%) | 19 (19%) | 8 (8%) | 9 (9%) | 8 (8%) | 100 |
pre_research_3 | 32 (32%) | 23 (23%) | 15 (15%) | 20 (20%) | 10 (10%) | 100 |
pre_research_4 | 9 (9%) | 24 (24%) | 32 (32%) | 24 (24%) | 11 (11%) | 100 |
pre_research_5 | 40 (40%) | 18 (18%) | 21 (21%) | 13 (13%) | 8 (8%) | 100 |
pre_research_6 | 17 (17%) | 25 (25%) | 25 (25%) | 16 (16%) | 17 (17%) | 100 |
pre_research_7 | 59 (59%) | 13 (13%) | 11 (11%) | 10 (10%) | 7 (7%) | 100 |
pre_research_8 | 21 (21%) | 19 (19%) | 23 (23%) | 19 (19%) | 18 (18%) | 100 |
freq_tables[["Ability Items"]]Question | Minimal | Slight | Moderate | Good | Extensive | n |
|---|---|---|---|---|---|---|
post_ability_1 | 3 (3%) | 20 (20%) | 23 (23%) | 20 (20%) | 34 (34%) | 100 |
post_ability_2 | 2 (2%) | 11 (11%) | 11 (11%) | 29 (29%) | 47 (47%) | 100 |
post_ability_3 | 11 (11%) | 19 (19%) | 22 (22%) | 15 (15%) | 33 (33%) | 100 |
post_ability_4 | 9 (9%) | 6 (6%) | 9 (9%) | 23 (23%) | 53 (53%) | 100 |
post_ability_5 | 12 (12%) | 19 (19%) | 16 (16%) | 27 (27%) | 26 (26%) | 100 |
post_ability_6 | 6 (6%) | 11 (11%) | 29 (29%) | 29 (29%) | 25 (25%) | 100 |
pre_ability_1 | 9 (9%) | 28 (28%) | 37 (37%) | 15 (15%) | 11 (11%) | 100 |
pre_ability_2 | 24 (24%) | 14 (14%) | 27 (27%) | 22 (22%) | 13 (13%) | 100 |
pre_ability_3 | 26 (26%) | 30 (30%) | 19 (19%) | 19 (19%) | 6 (6%) | 100 |
pre_ability_4 | 43 (43%) | 27 (27%) | 13 (13%) | 4 (4%) | 13 (13%) | 100 |
pre_ability_5 | 32 (32%) | 18 (18%) | 26 (26%) | 17 (17%) | 7 (7%) | 100 |
pre_ability_6 | 26 (26%) | 12 (12%) | 18 (18%) | 26 (26%) | 18 (18%) | 100 |
freq_tables[["Ethics Items"]]Question | Strongly | Disagree | Neither | Agree | Strongly | n |
|---|---|---|---|---|---|---|
post_ethics_1 | 4 (4%) | 18 (18%) | 24 (24%) | 30 (30%) | 24 (24%) | 100 |
post_ethics_2 | 7 (7%) | 10 (10%) | 13 (13%) | 31 (31%) | 39 (39%) | 100 |
post_ethics_3 | 7 (7%) | 26 (26%) | 14 (14%) | 28 (28%) | 25 (25%) | 100 |
post_ethics_4 | 10 (10%) | 7 (7%) | 6 (6%) | 19 (19%) | 58 (58%) | 100 |
post_ethics_5 | 3 (3%) | 14 (14%) | 25 (25%) | 31 (31%) | 27 (27%) | 100 |
pre_ethics_1 | 10 (10%) | 16 (16%) | 45 (45%) | 20 (20%) | 9 (9%) | 100 |
pre_ethics_2 | 20 (20%) | 15 (15%) | 27 (27%) | 23 (23%) | 15 (15%) | 100 |
pre_ethics_3 | 28 (28%) | 16 (16%) | 30 (30%) | 12 (12%) | 14 (14%) | 100 |
pre_ethics_4 | 50 (50%) | 17 (17%) | 12 (12%) | 6 (6%) | 15 (15%) | 100 |
pre_ethics_5 | 14 (14%) | 16 (16%) | 31 (31%) | 30 (30%) | 9 (9%) | 100 |
Grouped Demograpic Table
blackstone contains a function to create frequency
tables for demographics (combined demographics table) that can also be
grouped by a variable, like role or cohort as well:
groupedTable().
# Set up labels for variables
# Labels for questions column of table, pass to question_labels argument:
demos_labels <- c('Gender' = "gender",
'Race/Ethnicity' = "ethnicity",
'First-Generation College Student' = "first_gen")
sm_data %>% dplyr::select(gender, ethnicity, first_gen) %>% # select the demographic vars
blackstone::groupedTable(question_labels = demos_labels) # pass the new labels for the 'Question' column.Question | Response | n = 1001 |
|---|---|---|
Gender | ||
Female | 47 (47%) | |
Male | 50 (50%) | |
Non-binary | 2 (2%) | |
Do not wish to specify | 1 (1%) | |
Race/Ethnicity | ||
White (Non-Hispanic/Latino) | 36 (36%) | |
Asian | 23 (23%) | |
Black | 7 (7%) | |
Hispanic or Latino | 18 (18%) | |
American Indian or Alaskan Native | 5 (5%) | |
Native Hawaiian or other Pacific Islander | 7 (7%) | |
Do not wish to specify | 4 (4%) | |
First-Generation | ||
Yes | 59 (59%) | |
No | 39 (39%) | |
I'm not sure | 2 (2%) | |
1n (%) | ||
Statistical Inference: T-test or Wilcoxon test
In this section, we will run simple statistical tests using the
pipe-friendly functions contained in the {rstatix}
package.
Single Pre-Post Items
Running Normality Tests, then T-test or Wilcoxon test:
Since a large number of our surveys have a sample size smaller than 30, it is important to check the normality assumption before running any statistical tests. If the data is distributed normally we can use T-tests (parametric tests) for simple hypothesis testing of pre-post items, to see if any changes between the surveys are statistically significant.
If the data is not distributed normally (non-normal) then we will have to use non-parametric statistical tests, like the Wilcoxon test.
Determining if data is distributed normally includes both visual and statistical tests.
Normality Visualizations: Density and QQ (Quantile-Quantile) Plots
The ggpubr package contains functions to create density
and QQ plots to visually inspect if data is distributed normally.
# First create a difference score for the pre and post items:
sm_data <- sm_data %>% dplyr::mutate(knowledge_diff = post_knowledge_num - pre_knowledge_num) # get difference of pre and post scores
# Density plot:
ggpubr::ggdensity(sm_data, "knowledge_diff", fill = "lightgray")
# QQ plot:
ggpubr::ggqqplot(sm_data, "knowledge_diff")
If the data is normally distributed, the density plot would be shaped like a bell curve and the QQ plot would have all of the sample observations (points), lined up along the 45-degree reference line within the shaded confidence interval.
This data is probably not distributed normally, lets run a statistical test to confirm.
Next, run the Shapiro-Wilk’s test. The null hypothesis of this test is the sample distribution is normal. That means that if the test is significant (p-value < 0.05), the distribution is non-normal.
sm_data %>% rstatix::shapiro_test(knowledge_diff)
## # A tibble: 1 × 3
## variable statistic p
## <chr> <dbl> <dbl>
## 1 knowledge_diff 0.885 0.000000301rstatix::shapiro_test() returns a tibble with three
column, the column named p is the p-value for the Shapiro’s
test.
Data is not normally distributed for the knowledge items (since the p-value is < 0.05)- use a Wilcoxon test.
Wilcoxon test
There are a couple of ways to run a Wilcoxon test- either a
pipe-friendly version rstatix::wilcox_test() or base R has
wilcox.test where you must pass each variable as a numeric vector.
# Either use a pipe-friendly version of `wilcox_test()` from `rstatix`, need to covert to long form and have `timing` as a variable:
knowledge_wilcoxon <- sm_data %>% dplyr::select(tidyselect::contains("_knowledge") & tidyselect::contains("_num")) %>% # select the data
tidyr::pivot_longer(tidyselect::contains(c("pre_", "post_")), names_to = "question", values_to = "response") %>% # pivot to long-form
tidyr::separate(.data$question, into = c("timing", "question"), sep = "_", extra = "merge") %>% # Separate out the prefix to get timing
rstatix::wilcox_test(response ~ timing, paired = TRUE, detailed = TRUE) # Run the Wilcoxon test using column "response" (numeric values) on "timing" (pre or post)
knowledge_wilcoxon
## # A tibble: 1 × 12
## estimate .y. group1 group2 n1 n2 statistic p conf.low conf.high
## * <dbl> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 2.50 resp… post pre 100 100 3800. 1.23e-13 2.00 3.00
## # ℹ 2 more variables: method <chr>, alternative <chr>
# Or use the simple base R wilcox.test with each pre and post item:
wilcox.test(sm_data[["post_knowledge_num"]], sm_data[["pre_knowledge_num"]], paired = TRUE)
##
## Wilcoxon signed rank test with continuity correction
##
## data: sm_data[["post_knowledge_num"]] and sm_data[["pre_knowledge_num"]]
## V = 3800, p-value = 0.0000000000001
## alternative hypothesis: true location shift is not equal to 0Wilcoxon test is significant, there is a significant difference in pre and post scores of knowledge scores.
Composite Scales (multiple items)
Most of the surveys we conduct use composite scales of items that measure any underlying concept. These are average together to create a more reliable measure that can then be used in statistical inference.
Creating Composite Scores
Create composite scores for pre and post data by taking the mean of each set of items, and then get difference scores between pre and post mean:
sm_data <- sm_data %>% dplyr::rowwise() %>% # Get the mean for each individual by row
dplyr::mutate(pre_research_mean = mean(dplyr::c_across(tidyselect::contains("pre_research_") & tidyselect::contains("_num"))), # pre mean for each individual
post_research_mean = mean(dplyr::c_across(tidyselect::contains("post_research_") & tidyselect::contains("_num"))), # post mean for each individual
diff_research = post_research_mean - pre_research_mean # get difference scores of pre and post means.
) %>% dplyr::ungroup()Normality Testing of Composite Scales
Run a visual inspection of the difference scores between pre and post mean of the research items:
# Density plot:
ggpubr::ggdensity(sm_data, "diff_research", fill = "lightgray")
# QQ plot:
ggpubr::ggqqplot(sm_data, "diff_research")
Visually, the data appears normally distributed, Next, run the Shapiro-Wilk’s test to confirm.
sm_data %>% rstatix::shapiro_test(diff_research) # not significant, data likely normal
## # A tibble: 1 × 3
## variable statistic p
## <chr> <dbl> <dbl>
## 1 diff_research 0.991 0.720Data is normally distributed for the research composite items (since the p-values is > 0.05)- use a T-test.
T-test of Composite Scales
# Either use a pipe-friendly version of wilcox_test from `rstatix`, need to covert to long form and have `timing` as a variable:
research_t_test <- sm_data %>% dplyr::select(pre_research_mean, post_research_mean) %>% # select the pre and post means for research items
tidyr::pivot_longer(tidyselect::contains(c("pre_", "post_")), names_to = "question", values_to = "response") %>% # pivot to long-form
tidyr::separate(.data[["question"]], into = c("timing", "question"), sep = "_", extra = "merge") %>% # Separate out the prefix to get timing
rstatix::t_test(response ~ timing, paired = TRUE, detailed = TRUE)# Run the T-test using column "response" (numeric values) on "timing" (pre or post)
research_t_test
## # A tibble: 1 × 13
## estimate .y. group1 group2 n1 n2 statistic p df conf.low
## * <dbl> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 1.02 response post pre 100 100 15.6 2.43e-28 99 0.890
## # ℹ 3 more variables: conf.high <dbl>, method <chr>, alternative <chr>
# Or use the simple base R wilcox.test with each pre and post item:
t.test(sm_data[["post_research_mean"]], sm_data[["pre_research_mean"]], paired = TRUE)
##
## Paired t-test
##
## data: sm_data[["post_research_mean"]] and sm_data[["pre_research_mean"]]
## t = 16, df = 99, p-value <0.0000000000000002
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## 0.8899 1.1501
## sample estimates:
## mean difference
## 1.02T-test is significant, there is a mean difference in pre and post scores of 1.02.
The vignette Data Visualization will
explain how to create visuals using blackstone with the
example data analyzed in this vignette.
- End vignette:
Data analysis and Statistical Inferencename file: ‘analysis.Rmd’
Data Visualization
- Start of new vignette:
Data Visualizationname file: ‘data_visualization.Rmd’
blackstone contains many functions for data
visualization. This vignette shows you:
How to determine which color palettes to use in visuals.
How create stacked bar charts.
How create diverging stacked bar charts.
How create arrow charts.
Color Palettes
blackstone has functions that create 3 types of charts
for data visualization: stacked bar charts, diverging stacked bar
charts, and arrow charts.
The functions for stacked bar charts and diverging stacked bar charts can use two different color palettes: a blue sequential palette or a blue-red diverging color palette.
The blue sequential palette should be used for all likert scales that have one clear direction like: Not at all confident, Slightly confident, Somewhat confident, Very confident, Extremely confident
The blue-red diverging color palette should be used if the items have a likert scale that is folded or runs from a negative to positive valence like this: Strongly disagree, Disagree, Neither agree nor disagree, Agree, Strongly agree
The next three sections show examples on how to use these functions.
Stacked Bar Charts
The most common visual that is used with reporting at Blackstone
Research and Evaluation is a stacked bar chart, blackstone
has a function to that makes creating these charts fast and easy:
stackedBarChart().
stackedBarChart() takes in a tibble of factor/character
variables to turn into a stacked bar chart. The other requirement is a
character vector of scale labels for the likert scale that makes up the
items in the tibble (same as the one use to set them up as factors in
the data cleaning section).
Pre-post Stacked Bar Chart with Overall n and Percentages
- By default,
stackedBarChart()uses the blue sequential palette to color the bars and sorts the items by the ones with the highest post items with the highest counts/percentages.
# Research Items scale:
levels_confidence <- c("Not at all confident", "Slightly confident", "Somewhat confident", "Very confident", "Extremely confident")
# select variables and pass them to `stackedBarChart()` along with scale_labels.
sm_data %>% dplyr::select(tidyselect::contains("research_") & !tidyselect::contains("_num") & where(is.factor)) %>% # select the factor variables for the research items
blackstone::stackedBarChart(., scale_labels = levels_confidence, pre_post = TRUE)
Pre-post Stacked Bar Chart with Individual Item n and Counts
# Select variables and pass them to `stackedBarChart()` along with scale_labels, change the arguements `percent_label` and `overall_n` both to FALSE:
sm_data %>% dplyr::select(tidyselect::contains("research_") & !tidyselect::contains("_num") & where(is.factor)) %>% # select the factor variables for the research items
blackstone::stackedBarChart(., scale_labels = levels_confidence, pre_post = TRUE, percent_label = FALSE, overall_n = FALSE)
Pre-post Stacked Bar Chart with Blue-Red Diverging Color Palette
## Ethics Items scale:
levels_agree5 <- c("Strongly disagree", "Disagree", "Neither agree nor disagree", "Agree", "Strongly agree")
# select variables and pass them to `stackedBarChart()` along with scale_labels,
# change `fill_colors` to "div" to use the blue-red diverging color palette:
sm_data %>% dplyr::select(tidyselect::contains("ethics_") & !tidyselect::contains("_num") & # select the factor variables for the ethics items
!tidyselect::contains("_oe") & where(is.factor)) %>%
blackstone::stackedBarChart(., scale_labels = levels_agree5, pre_post = TRUE, fill_colors = "div")
Pre-post Stacked Bar Chart with New Question Labels and Order:
# Question labels as a named vector with the naming structure
# like this: c("new label" = "original variable name"), where the
# names are the new question labels and the old names are the values without pre or post prefixes:
# Here I will use paste0 to create 8 research items like they appear without prefixes:
research_question_labels <- paste0(paste0("research_", 1:8))
# Set new labels as names of `research_question_labels`
names(research_question_labels) <- c("Research relevant background literature", "Identify a scientific problem",
"Develop testable and realistic research questions", "Develop a falsifiable hypothesis",
"Conduct quantitative data analysis", "Design an experiment/Create a research design",
"Interpret findings and making recommendations", "Scientific or technical writing")
# select variables and pass them to `stackedBarChart()` along with scale_labels, also pass research_question_labels to `question_labels` and set `question_order` to TRUE.
sm_data %>% dplyr::select(tidyselect::contains("research_") & !tidyselect::contains("_num") & where(is.factor)) %>% # select the factor variables for the research items
blackstone::stackedBarChart(., scale_labels = levels_confidence, pre_post = TRUE, question_labels = research_question_labels, question_order = TRUE)
Single time point Stacked Bar Chart with New Question Labels and Order
# Question labels as a named vector with the naming structure
# like this: c("new label" = "original variable name"), where the
# names are the new question labels and the old names are the values without pre or post prefixes:
# Here I will use paste0 to create 8 research items like they appear without prefixes:
research_question_labels <- paste0(paste0("post_research_", 1:8))
# Set new labels as names of `research_question_labels`
names(research_question_labels) <- c("Research relevant background literature", "Identify a scientific problem",
"Develop testable and realistic research questions", "Develop a falsifiable hypothesis",
"Conduct quantitative data analysis", "Design an experiment/Create a research design",
"Interpret findings and making recommendations", "Scientific or technical writing")
# select variables and pass them to `stackedBarChart()` along with scale_labels, set pre_post to FALSE (default),
# also pass research_question_labels to `question_labels` and set `question_order` to TRUE.
sm_data %>% dplyr::select(tidyselect::contains("post_research_") & !tidyselect::contains("_num") & where(is.factor)) %>% # select the factor variables for the research items
blackstone::stackedBarChart(., scale_labels = levels_confidence, question_labels = research_question_labels, question_order = TRUE)
Diverging Stacked Bar Charts
Another common visual that is used with reporting at Blackstone
Research and Evaluation is a diverging stacked bar chart, which I will
refer to from now on as a diverging bar chart. blackstone
has a function to make this type of chart, it is called:
divBarChart().
The diverging bar charts created using divBarChart(),
diverge just after the mid-point of the likert scale of the items
supplied to the function. See examples below.
divBarChart() has all of the same arguments as
stackedBarChart(), so using it has the same
requirements.
Pre-post Diverging Bar Chart with Overall n and Percentages
- By default,
divBarChart()uses the blue sequential palette to color the bars and sorts the items by the ones with the highest post items with the highest counts/percentages.
# Research Items scale:
levels_confidence <- c("Not at all confident", "Slightly confident", "Somewhat confident", "Very confident", "Extremely confident")
# select variables and pass them to `divBarChart()` along with scale_labels.
sm_data %>% dplyr::select(tidyselect::contains("research_") & !tidyselect::contains("_num") & where(is.factor)) %>% # select the factor variables for the research items
blackstone::divBarChart(., scale_labels = levels_confidence, pre_post = TRUE)
Pre-post Diverging Bar Chart with Individual Item n and Counts
# Select variables and pass them to `divBarChart()` along with scale_labels, change the arguements `percent_label` and `overall_n` both to FALSE:
sm_data %>% dplyr::select(tidyselect::contains("research_") & !tidyselect::contains("_num") & where(is.factor)) %>% # select the factor variables for the research items
blackstone::divBarChart(., scale_labels = levels_confidence, pre_post = TRUE, percent_label = FALSE, overall_n = FALSE)
Pre-post Diverging Bar Chart with Blue-Red Diverging Color Palette
## Ethics Items scale:
levels_agree5 <- c("Strongly disagree", "Disagree", "Neither agree nor disagree", "Agree", "Strongly agree")
# select variables and pass them to `divBarChart()` along with scale_labels,
# change `fill_colors` to "div" to use the blue-red diverging color palette:
sm_data %>% dplyr::select(tidyselect::contains("ethics_") & !tidyselect::contains("_num") & # select the factor variables for the ethics items
!tidyselect::contains("_oe") & where(is.factor)) %>%
blackstone::divBarChart(., scale_labels = levels_agree5, pre_post = TRUE, fill_colors = "div")
Pre-post Stacked Bar Chart with New Question Labels and Order
# Question labels as a named vector with the naming structure
# like this: c("new label" = "original variable name"), where the
# names are the new question labels and the old names are the values without pre or post prefixes:
# Here I will use paste0 to create 8 research items like they appear without prefixes:
research_question_labels <- paste0(paste0("research_", 1:8))
# Set new labels as names of `research_question_labels`
names(research_question_labels) <- c("Research relevant background literature", "Identify a scientific problem",
"Develop testable and realistic research questions", "Develop a falsifiable hypothesis",
"Conduct quantitative data analysis", "Design an experiment/Create a research design",
"Interpret findings and making recommendations", "Scientific or technical writing")
# select variables and pass them to `divBarChart()` along with scale_labels, also pass research_question_labels to `question_labels` and set `question_order` to TRUE.
sm_data %>% dplyr::select(tidyselect::contains("research_") & !tidyselect::contains("_num") & where(is.factor)) %>% # select the factor variables for the research items
blackstone::divBarChart(., scale_labels = levels_confidence, pre_post = TRUE, question_labels = research_question_labels, question_order = TRUE)
Single time point Stacked Bar Chart with New Question Labels and Order
# Question labels as a named vector with the naming structure
# like this: c("new label" = "original variable name"), where the
# names are the new question labels and the old names are the values without pre or post prefixes:
# Here I will use paste0 to create 8 research items like they appear without prefixes:
research_question_labels <- paste0(paste0("post_research_", 1:8))
# Set new labels as names of `research_question_labels`
names(research_question_labels) <- c("Research relevant background literature", "Identify a scientific problem",
"Develop testable and realistic research questions", "Develop a falsifiable hypothesis",
"Conduct quantitative data analysis", "Design an experiment/Create a research design",
"Interpret findings and making recommendations", "Scientific or technical writing")
# select variables and pass them to `divBarChart()` along with scale_labels, set pre_post to FALSE (default),
# also pass research_question_labels to `question_labels` and set `question_order` to TRUE.
sm_data %>% dplyr::select(tidyselect::contains("post_research_") & !tidyselect::contains("_num") & where(is.factor)) %>% # select the factor variables for the research items
blackstone::divBarChart(., scale_labels = levels_confidence, question_labels = research_question_labels, question_order = TRUE)
Arrow Charts
Arrow charts show the difference in means at two time points,
blackstone has two functions that create arrow charts:
arrowChart() and arrowChartGroup().
Both use a tibble of numeric pre-post data as the main input, and also require a character vector of scale labels for the numeric scale that makes up the items in the tibble. The rest of the arguments for the two arrow chart functions are the sames as the stacked bar chart functions.
arrowChart()
Arrow Chart with defaults
- By default,
arrowChart()sorts the items/arrows by the ones with the highest post average on down and the arrows are the dark blue color hex code #283251.
# Research Items scale:
levels_confidence <- c("Not at all confident", "Slightly confident", "Somewhat confident", "Very confident", "Extremely confident")
# select variables and pass them to `divBarChart()` along with scale_labels.
sm_data %>% dplyr::select(tidyselect::contains("research_") & tidyselect::contains("_num") & where(is.numeric)) %>% # select the numeric variables for the research items
blackstone::arrowChart(., scale_labels = levels_confidence)
Arrow Chart with Individual Item n
# Select variables and pass them to `divBarChart()` along with scale_labels, change the arguement `overall_n` both to FALSE:
sm_data %>% dplyr::select(tidyselect::contains("research_") & tidyselect::contains("_num") & where(is.numeric)) %>% # select the numeric variables for the research items
blackstone::arrowChart(., scale_labels = levels_confidence, overall_n = FALSE)
Arrow Chart with New Question Labels and Order
# Question labels as a named vector with the naming structure
# like this: c("new label" = "original variable name"), where the
# names are the new question labels and the old names are the values without pre or post prefixes:
# Here I will use paste0 to create 8 research items like they appear without prefixes:
research_question_labels <- paste0(paste0("research_", 1:8))
# Set new labels as names of `research_question_labels`
names(research_question_labels) <- c("Research relevant background literature", "Identify a scientific problem",
"Develop testable and realistic research questions", "Develop a falsifiable hypothesis",
"Conduct quantitative data analysis", "Design an experiment/Create a research design",
"Interpret findings and making recommendations", "Scientific or technical writing")
# Select variables and pass them to `arrowChart()` along with scale_labels, and also pass research_question_labels to `question_labels` and set `question_order` to TRUE:
sm_data %>% dplyr::select(tidyselect::contains("research_") & tidyselect::contains("_num") & where(is.numeric)) %>% # select the numeric variables for the research items
blackstone::arrowChart(., scale_labels = levels_confidence, question_labels = research_question_labels, question_order = TRUE)
arrowChartGroup()
arrowChartGroup() allows the user to create an arrow
chart of pre-post averages grouped by a third variable, while also
showing the overall pre-post average as an arrow.
Arrow Chart by Group with defaults
- By default,
arrowChartGroup()sorts the items/arrows by the ones with the highest post average on down and the arrows are colored using the Qualitative Color Palette, which has 11 distinct colors:- #E69F00, #56B4E9, #009E73, #CC79A7, #D55E00, #0072B2, #440154FF,
#999999, #117733, #283251, #999933
- #E69F00, #56B4E9, #009E73, #CC79A7, #D55E00, #0072B2, #440154FF,
#999999, #117733, #283251, #999933
arrowChartGroup()returns pre-post averages for each group passed togroup_levelsas well as an “Overall” which is the whole sample and will always be the color black, also the order ofgroup_levelswill also determining the order of the arrows and legend.
# Research Items scale:
levels_confidence <- c("Not at all confident", "Slightly confident", "Somewhat confident", "Very confident", "Extremely confident")
# select variables and pass them to `arrowChartGroup()` along with scale_labels, the grouping variable in `group` and the levels for each group in `group_levels`:
sm_data %>% dplyr::select(gender, tidyselect::contains("research_") & tidyselect::contains("_num") & where(is.numeric)) %>% # select the numeric variables for the research items
blackstone::arrowChartGroup(., group = "gender", group_levels = gender_levels, scale_labels = levels_confidence)
Arrow Chart with Individual Item n
# Select variables and pass them to `divBarChart()` along with scale_labels, change the argument `overall_n` both to FALSE:
sm_data %>% dplyr::select(gender, tidyselect::contains("research_") & tidyselect::contains("_num") & where(is.numeric)) %>% # select the numeric variables for the research items
blackstone::arrowChartGroup(., group = "gender", group_levels = gender_levels,scale_labels = levels_confidence, overall_n = FALSE)
Arrow Chart with New Question Labels and Order
# Question labels as a named vector with the naming structure
# like this: c("new label" = "original variable name"), where the
# names are the new question labels and the old names are the values without pre or post prefixes:
# Here I will use paste0 to create 8 research items like they appear without prefixes:
research_question_labels <- paste0(paste0("research_", 1:8))
# Set new labels as names of `research_question_labels`
names(research_question_labels) <- c("Research relevant background literature", "Identify a scientific problem",
"Develop testable and realistic research questions", "Develop a falsifiable hypothesis",
"Conduct quantitative data analysis", "Design an experiment/Create a research design",
"Interpret findings and making recommendations", "Scientific or technical writing")
# Select variables and pass them to `arrowChart()` along with scale_labels, and also pass research_question_labels to `question_labels` and set `question_order` to TRUE:
sm_data %>% dplyr::select(gender, tidyselect::contains("research_") & tidyselect::contains("_num") & where(is.numeric)) %>% # select the numeric variables for the research items
blackstone::arrowChartGroup(., group = "gender", group_levels = gender_levels,scale_labels = levels_confidence, question_labels = research_question_labels, question_order = TRUE)
- End vignette:
Data Visualizationname file: ‘data_visualization.Rmd’